Simultaneous graphical dynamic linear models (SGDLMs) provide advances in flexibility, parsimony and scalability of multivariate time series analysis, with proven utility in forecasting. Core theoretical aspects of such models are developed, including new results linking dynamic graphical and latent factor models. Methodological developments extend existing Bayesian sequential analyses for model marginal likelihood evaluation and counterfactual forecasting. The latter, involving new Bayesian computational developments for missing data in SGDLMs, is motivated by causal applications. A detailed example illustrating the models and new methodology concerns global macroeconomic time series with complex, time-varying cross-series relationships and primary interests in potential causal effects.

翻译：同步图动态线性模型（SGDLMs）在多元时间序列分析的灵活性、简约性和可扩展性方面取得了进展，其预测效用已得到验证。本文发展了此类模型的核心理论，包括连接动态图模型与潜在因子模型的新结果。方法论上的进展扩展了现有的贝叶斯序贯分析，用于模型边际似然评估和反事实预测。后者涉及SGDLMs中缺失数据的贝叶斯计算新方法，其动机源于因果推断应用。一个详细示例说明了模型与新方法在全局宏观经济时间序列中的应用，该序列具有复杂的时变跨序列关系，且主要关注潜在因果效应。